Teaching and assessment of prescribing medicines : how does pharmacy perform?

BACKGROUND Prescribing is a complex task, requiring specific knowledge and skills combined with effective, context-specific clinical reasoning. Prescribing errors can result in significant morbidity and mortality. For all professions with prescribing rights, a clear need exists to ensure students graduate with a well-defined set of prescribing skills, which will contribute to competent prescribing. AIM To describe the methods employed to teach and assess the principles of effective prescribing across five non-medical professions at Queensland University of Technology. METHOD The NPS National Prescribing Competencies Framework (PCF) was used as the prescribing standard. A curriculum mapping exercise was undertaken to determine how well the PCF was addressed across the disciplines of paramedic science, pharmacy, podiatry, nurse practitioner and optometry. Identified gaps in teaching and/or assessment were noted. RESULTS Prescribing skills and knowledge are taught and assessed using a range of methods across disciplines. A multi-modal approach is employed by all disciplines. The Pharmacy discipline uses more tutorial sessions to teach prescribing principles and relies less on case studies and clinical appraisal to assess prescribing when compared to other disciplines. Within the pharmacy discipline approximately 90% of the PCF competencies are taught and assessed. This compares favourably with the other disciplines. CONCLUSION Further work is required to establish a practical, effective approach to the assessment of prescribing competence especially between the university and clinical settings. Effective and reliable assessment of prescribing undertaken by students in diverse settings remains challenging.

A framework for model integration and holistic modelling of socio-technical systems

This paper presents a layered framework for the purposes of integrating different Socio-Technical Systems (STS) models and perspectives into a whole-of-systems model. Holistic modelling plays a critical role in the engineering of STS due to the interplay between social and technical elements within these systems and resulting emergent behaviour. The framework decomposes STS models into components, where each component is either a static object, dynamic object or behavioural object. Based on existing literature, a classification of the different elements that make up STS, whether it be a social, technical or a natural environment element, is developed; each object can in turn be classified according to the STS elements it represents. Using the proposed framework, it is possible to systematically decompose models to an extent such that points of interface can be identified and the contextual factors required in transforming the component of one model to interface into another is obtained. Using an airport inbound passenger facilitation process as a case study socio-technical system, three different models are analysed: a Business Process Modelling Notation (BPMN) model, Hybrid Queue-based Bayesian Network (HQBN) model and an Agent Based Model (ABM). It is found that the framework enables the modeller to identify non-trivial interface points such as between the spatial interactions of an ABM and the causal reasoning of a HQBN, and between the process activity representation of a BPMN and simulated behavioural performance in a HQBN. Such a framework is a necessary enabler in order to integrate different modelling approaches in understanding and managing STS.

Statistics at play

Three core components in developing children’s understanding and appreciation of data — establish a context, pose and answer statistical questions, represent and interpret data — lay the foundation for the fourth component: use data to enhance existing context.

Developing young students’ meta-representational competence through integrated mathematics and science investigations

This paper describes students’ developing meta-representational competence, drawn from the second phase of a longitudinal study, Transforming Children’s Mathematical and Scientific Development. A group of 21 highly able Grade 1 students was engaged in mathematics/science investigations as part of a data modelling program. A pedagogical approach focused on students’ interpretation of categorical and continuous data was implemented through researcher-directed weekly sessions over a 2-year period. Fine-grained analysis of the developmental features and explanations of their graphs showed that explicit pedagogical attention to conceptual differences between categorical and continuous data was critical to development of inferential reasoning.

Clustering and labeling a web scale document collection using Wikipedia clusters

Clustering is an important technique in organising and categorising web scale documents. The main challenges faced in clustering the billions of documents available on the web are the processing power required and the sheer size of the datasets available. More importantly, it is nigh impossible to generate the labels for a general web document collection containing billions of documents and a vast taxonomy of topics. However, document clusters are most commonly evaluated by comparison to a ground truth set of labels for documents. This paper presents a clustering and labeling solution where the Wikipedia is clustered and hundreds of millions of web documents in ClueWeb12 are mapped on to those clusters. This solution is based on the assumption that the Wikipedia contains such a wide range of diverse topics that it represents a small scale web. We found that it was possible to perform the web scale document clustering and labeling process on one desktop computer under a couple of days for the Wikipedia clustering solution containing about 1000 clusters. It takes longer to execute a solution with finer granularity clusters such as 10,000 or 50,000. These results were evaluated using a set of external data.

Establishing statistical foundations early : data modeling with young learners

This paper addresses research from a three-year longitudinal study that engaged children in data modeling experiences from the beginning school year through to third year (6-8 years). A data modeling approach to statistical development differs in several ways from what is typically done in early classroom experiences with data. In particular, data modeling immerses children in problems that evolve from their own questions and reasoning, with core statistical foundations established early. These foundations include a focus on posing and refining statistical questions within and across contexts, structuring and representing data, making informal inferences, and developing conceptual, representational, and metarepresentational competence. Examples are presented of how young learners developed and sustained informal inferential reasoning and metarepresentational competence across the study to become “sophisticated statisticians”.

Kids, counselors and troubles-telling : morality-in-action in talk on an Australian children’s helpline

This article begins with the premise that morality is an intrinsic, although often invisible, aspect of everyday social action. Drawn from a corpus of fifty audiorecorded telephone calls to Kids Helpline, an Australian helpline for children and young people, we examine one call to show how the young caller and counsellor co-construct ‘morality-in-action’. Ethnomethodological understandings and, in particular, Sacks’ (1992) description of ‘Class 2’ rules and infractions show how an adolescent caller and counsellor collaboratively assemble moral versions of the caller. In puzzling out possible motives, the caller and counsellor can be seen to be attending to the implications of different moral versions of the caller. This attribution of motives is moral work in action, with motives contingently assembled, displayed and evaluated, with such work understood as displays of moral reasoning. The counselling call makes visible the counsellor’s interactional work to support and empower the client. Analysis such as this offers counsellors ways of understanding and making visible their interactional and moral work within helpline call interactions.

Learning through modelling in the primary years

Many nations are highlighting the need for a renaissance in the mathematical sciences as essential to the well-being of all citizens (e.g., Australian Academy of Science, 2006; 2010; The National Academies, 2009). Indeed, the first recommendation of The National Academies’ Rising Above the Storm (2007) was to vastly improve K–12 science and mathematics education. The subsequent report, Rising Above the Gathering Storm Two Years Later (2009), highlighted again the need to target mathematics and science from the earliest years of schooling: “It takes years or decades to build the capability to have a society that depends on science and technology . . . You need to generate the scientists and engineers, starting in elementary and middle school” (p. 9). Such pleas reflect the rapidly changing nature of problem solving and reasoning needed in today’s world, beyond the classroom. As The National Academies (2009) reported, “Today the problems are more complex than they were in the 1950s, and more global. They’ll require a new educated workforce, one that is more open, collaborative, and cross-disciplinary” (p. 19). The implications for the problem solving experiences we implement in schools are far-reaching. In this chapter, I consider problem solving and modelling in the primary school, beginning with the need to rethink the experiences we provide in the early years. I argue for a greater awareness of the learning potential of young children and the need to provide stimulating learning environments. I then focus on data modelling as a powerful means of advancing children’s statistical reasoning abilities, which they increasingly need as they navigate their data-drenched world.

Instance-driven TBox revision in DL-Lite

The development and maintenance of large and complex ontologies are often time-consuming and error-prone. Thus, automated ontology learning and revision have attracted intensive research interest. In data-centric applications where ontologies are designed or automatically learnt from the data, when new data instances are added that contradict to the ontology, it is often desirable to incrementally revise the ontology according to the added data. This problem can be intuitively formulated as the problem of revising a TBox by an ABox. In this paper we introduce a model-theoretic approach to such an ontology revision problem by using a novel alternative semantic characterisation of DL-Lite ontologies. We show some desired properties for our ontology revision. We have also developed an algorithm for reasoning with the ontology revision without computing the revision result. The algorithm is efficient as its computational complexity is in coNP in the worst case and in PTIME when the size of the new data is bounded.

Theorizing why in digital learning: Opening frontiers for inquiry and innovation with technology

Asking why is an important foundation of inquiry and fundamental to the development of reasoning skills and learning. Despite this, and despite the relentless and often disruptive nature of innovations in information and communications technology (ICT), sophisticated tools that directly support this basic act of learning appear to be undeveloped, not yet recognized, or in the very early stages of development. Why is this so? To this question, there is no single factual answer. In response, however, plausible explanations and further questions arise, and such responses are shown to be typical consequences of why-questioning. A range of contemporary scenarios are presented to highlight the problem. Consideration of the various inputs into the evolution of digital learning is introduced to provide historical context and this serves to situate further discussion regarding innovation that supports inquiry-based learning. This theme is further contextualized by narratives on openness in education, in which openness is also shown to be an evolving construct. Explanatory and descriptive contents are differentiated in order to scope out the kinds of digital tools that might support inquiry instigated by why-questioning and which move beyond the search paradigm. Probing why from a linguistic perspective reveals versatile and ambiguous semantics. The why dimension—asking, learning, knowing, understanding, and explaining why—is introduced as a construct that highlights challenges and opportunities for ICT innovation. By linking reflective practice and dialogue with cognitive engagement, this chapter points to specific frontiers for the design and development of digital learning tools, frontiers in which inquiry may find new openings for support.

Moral vitalism: Seeing good and evil as real, agentic forces

Moral vitalism refers to a tendency to view good and evil as actual forces that can influence people and events. We introduce a scale designed to assess the belief in moral vitalism. High scorers on the scale endorse items such as “There are underlying forces of good and evil in this world”. After establishing the reliability and criterion validity of the scale (Studies 1, 2a, 2b), we examined the predictive validity of the moral vitalism scale, showing that “moral vitalists” worry about being possessed by evil (Study 3), being contaminated through contact with evil people (Study 4), and forfeiting their own mental purity (Study 5). We discuss the nature of moral vitalism and the implications of the construct for understanding the role of metaphysical lay theories about the nature of good and evil in moral reasoning.

Using the TARL Model in Psychology: Supporting First and Final Year Students to Compose Reflections

Reflective thinking is an important skill in psychology, both as a tool in the therapeutic process and in professional development. The adapted 4Rs Model provides reflective writing structure and highlights the importance of Reporting & Responding, Relating, Reasoning and Reconstructing to students who are new to writing reflections. This chapter presents a case in which the 4Rs model (modified from the 5Rs model in Chap. 2 of this edition) was adopted to support reflective writing skills of undergraduate psychology students in a first year unit and in a final year unit. Although all students reflected on their learning within the units, the support activities leading up to the reflective tasks were adjusted to account for differences in the abilities of the cohorts and the focus of the units. In an evaluation survey, both groups of students endorsed statements about the importance of reflections and the utility of using the model. First year students also reported some difficulties understanding the 4Rs. This chapter will explore how first and final year students can be supported to develop reflection skills through scaffolding and modification of the same approaches and model.

U v U [2002] HCA 36: Commentary

This commentary offers a feminist analysis of relocation cases through the lens of U v U [2002] HCA 36, and with reference to the re-written judgment for the Australian Feminist Judgments project. First, the commentary considers the gendered nature of relocation cases, and analyses aspects of the reasoning and outcome of U v U that are of concern from a feminist perspective. Second, the commentary discusses how the re-written judgment addresses these concerns, thereby offering a feminist judgment on the issue of relocation in family law.

Fuzzy-DL perception for multi-robot systems

For future planetary robot missions, multi-robot-systems can be considered as a suitable platform to perform space mission faster and more reliable. In heterogeneous robot teams, each robot can have different abilities and sensor equipment. In this paper we describe a lunar demonstration scenario where a team of mobile robots explores an unknown area and identifies a set of objects belonging to a lunar infrastructure. Our robot team consists of two exploring scout robots and a mobile manipulator. The mission goal is to locate the objects within a certain area, to identify the objects, and to transport the objects to a base station. The robots have a different sensor setup and different capabilities. In order to classify parts of the lunar infrastructure, the robots have to share the knowledge about the objects. Based on the different sensing capabilities, several information modalities have to be shared and combined by the robots. In this work we propose an approach using spatial features and a fuzzy logic based reasoning for distributed object classification.

An application of fuzzy DL-Based semantic perception to soil container classification

Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments.